The fuzzy c-means (FCM) is a frequently utilized algorithm at present. Yet, the clustering quality and convergence rate of FCM are determined by the initial cluster centers, and so an improved FCM algorithm based on canopy cluster concept to quickly analyze the dataset has been proposed. Taking advantage of the canopy algorithm for its rapid acquisition of cluster centers, this algorithm regards the cluster results of canopy as the input. In this way, the convergence rate of the FCM algorithm is accelerated. Meanwhile, the MapReduce scheme of the proposed FCM algorithm is designed in a cloud environment. Experimental results demonstrate the hybrid canopy-FCM clustering algorithm processed by MapReduce be endowed with better clustering quality and higher operation speed.

Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, one of the challenging problems is the process in task scheduling after task allocation by task clustering. We propose a task scheduling method after task clustering, leveraging worst schedule length (WSL) as an upper bound of the schedule length. In our proposed method, a task in a WSL sequence is scheduled preferentially to make the WSL smaller. Experimental results by simulation show that the response time is improved in several task clustering heuristics. In particular, our proposed scheduling method with the task clustering outperforms conventional list-based task scheduling methods.

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.

Navigating a large 3D virtual environment using a generic haptic device can be challenging since the haptic device is usually bounded by its own physical workspace. On the other hand, mouse interaction easily handles the situation with a clutching mechanism-simply lifting the mouse and repositioning its location in the physical space. Since the haptic device is used for both input and output at the same time, in many cases, its freedom needs to be limited in order to accommodate such a situation. In this paper, we propose a new mechanism called Z-Clutching for 3D navigation of a virtual environment by using only the haptic device without any interruption or sacrifice in the given degrees of freedom of the device`s handle. We define the clutching state which is set by pulling the haptic handle back into space. It acts similarly to lifting the mouse off the desk. In this way, the user naturally feels the haptic feedback based on the depth (z-direction), while manipulating the haptic device and moving the view as desired. We conducted a user study to evaluate the proposed interaction technique, and the results are promising in terms of the usefulness of the proposed mechanism.